ABSTRACT- In establishing restoration goals it is difficult to choose both a single time period applicable to all management needs and a geographical extent that accurately characterizes system dynamics. To avoid these pitfalls requires an approach that integrates multiple data sources spanning several temporal and spatial scales. The goal of our study was to describe the natural range of variation in northern-hardwood and hemlock-hardwood forests of the northern Great Lakes states, USA, using historical data and empirical field data at two spatial scales, that of the landscape and the forested stand. Determining reference conditions based on landscapes free of all human influence is not a possibility, thus historical data provided an understanding of forest parameters prior to Euro-American settlement. Still, it is not clear that forest characteristics as determined by historical analyses are resilient enough to withstand current conditions and endure through future surprises. Therefore we synthesized existing, current data on the structural components, spatial characteristics, and biogeochemical processes of remnant old-growth forests with historical data sources to establish baseline variability. Using this method we suggest a set of reference conditions that bracket a range of possible outcomes in order to guide restoration goals. These reference conditions are then compared to the range of variability detected in the same parameters measured in managed forests of the region. For some parameters, such as net nitrogen mineralization rates, management practices resulted in conditions similar to expectations based on the established reference conditions. However, forest management significantly altered other parameters, like the amount of coarse woody debris. Although, our analyses were limited to specific forest types within a limited geographic region, we suggest that our approach has wider application as it provides a conceptual framework useful for managing dynamic forested ecosystems that are vulnerable to uncertainties due to future human influences.